Genome-partitioning of genetic variation for complex traits using common SNPs

By Jian Yang, Teri A. Manolio, L. Pasquale, Eric Boerwinkle, Neil C Caporaso, Julie M. Cunningham, M. Andrade, B. Feenstra, Eleanor Feingold, M. Hayes and 14 more. Nature Genetics, 2011

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We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that ∼45%, ∼17%, ∼25% and ∼21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further ∼0.5–1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/05f0b1a26f3bb86326b7e4172d7a6cfd2bcfff3f

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Citations in the corpus

Efficient Bayesian mixed model analysis increases association power in large cohorts (2014, background): “Note that h 2 pseudo can be larger than h(2)g (the heritability explained by genotyped SNPs [39]) due to cryptic relatedness or population structure [48], but for samples such as WGHS without substantial relatedness or population”
Multiple Sclerosis Genomic Map implicates peripheral immune cells & microglia in susceptibility (2019, result): “Heritability partitioning by using minor allele frequency or P value thresholds has led to substantial insights in previous studies (21), and we therefore applied a similar partitioning approach but in a fashion that took into consideration the study design and the existence of replication informati”
Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all! (2012, background): “…of SNPs in genetic risk model has continued to resort on GRSs (Cui, 2009; Goddard et al., 2009; Kooperberg et al., 2009; Purcell et al., 2009; Yang et al., 2010; Chen et al., 2011; Chibnik et al., 2011), while machine learning type methods continue to be rare regardless of some successful…”
Pitfalls of predicting complex traits from SNPs (2013, methodology): “We used data of 11,586 unrelated European Americans genotyped on 563,212 SNPs 71–73 .”
Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits (2012, background): “If there is only one SNP to be tested in the conditional analysis (N2 = 1), then equations (17) and (18) simplify to”
All SNPs Are Not Created Equal: Genome-Wide Association Studies Reveal a Consistent Pattern of Enrichment among Functionally Annotated SNPs (2013, background): “are detectable in GWAS data [5,6], particularly in and around”
Defining the role of common variation in the genomic and biological architecture of adult human height (2014, positive): “We combined height summary association statistics from 37 independent studies genotyped using Illumina’s Metabochip array 9 in a meta-analysis of 80,067 individuals of European ancestry (Supplementary Tables 20–22).”
The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits (2012, background): “Recent data emerging from theoretical models [1,2] and empirical observation through genome-wide association studies (GWAS) (for example [3,4]) demonstrate that hundreds of genetic loci contribute to complex traits in humans.”